Summary of 2_DecisionTree
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Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- num_class: 4
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
23.7 seconds
Metric details
|
0 |
1 |
2 |
3 |
accuracy |
macro avg |
weighted avg |
logloss |
| precision |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
3e-06 |
| recall |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
3e-06 |
| f1-score |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
3e-06 |
| support |
28 |
8 |
17 |
5 |
1 |
58 |
58 |
3e-06 |
Confusion matrix
|
Predicted as 0 |
Predicted as 1 |
Predicted as 2 |
Predicted as 3 |
| Labeled as 0 |
28 |
0 |
0 |
0 |
| Labeled as 1 |
0 |
8 |
0 |
0 |
| Labeled as 2 |
0 |
0 |
17 |
0 |
| Labeled as 3 |
0 |
0 |
0 |
5 |
Learning curves
Decision Tree
Tree #1
Rules
if (cp <= 0.5) then class: 0 (proba: 100.0%) | based on 83 samples
if (cp > 0.5) and (cp > 1.5) and (cp <= 2.5) then class: 2 (proba: 100.0%) | based on 50 samples
if (cp > 0.5) and (cp <= 1.5) then class: 1 (proba: 100.0%) | based on 23 samples
if (cp > 0.5) and (cp > 1.5) and (cp > 2.5) then class: 3 (proba: 100.0%) | based on 15 samples
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Precision Recall Curve
SHAP Importance
SHAP Dependence plots
Dependence 0 (Fold 1)
Dependence 1 (Fold 1)
Dependence 2 (Fold 1)
Dependence 3 (Fold 1)
SHAP Decision plots
Worst decisions for selected sample 1 (Fold 1)
Worst decisions for selected sample 2 (Fold 1)
Worst decisions for selected sample 3 (Fold 1)
Worst decisions for selected sample 4 (Fold 1)
Best decisions for selected sample 1 (Fold 1)
Best decisions for selected sample 2 (Fold 1)
Best decisions for selected sample 3 (Fold 1)
Best decisions for selected sample 4 (Fold 1)
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